Method of predicting project outcomes

ABSTRACT

The disclosed subject matter provides for a method and system for predicting project outcomes. The present method and system for predicting project outcomes aids a project manager or CEO in quickly determining if projects are on track to be completed as scheduled, and what areas or personnel need assistance in meeting their project goals and deadlines.

FIELD OF THE INVENTION

The present disclosure relates to project management. More specifically,the present disclosure relates to predicting project outcomes. It is tobe understood that it also finds application in other usage scenariosand is not necessarily limited to the aforementioned application.

BACKGROUND OF THE INVENTION

Generally, project managers need to know if a goal will be completed bythe designated time. Accurately predicting the success or failure ofcompleting a targeted goal allows managers to move resources to ensurethe successful completion of the goal CEO's are often required topredict the outcomes of company-wide goals each quarter.

Current methods project management make heavy use of percent completeestimates (PC). In PC methods, project participants regularly update thepercent of the project they have completed. These estimates only showthe percentage completed of the project as planned prior to starting theproject. These PC methods can be misleading due to the lack of otherinformation, such as what quality of work is going into completing theproject, or whether the project participants expect the project to becompleted on time.

PC methods for project management can also encourage false reporting byproject participants. In the PC method, project managers, superiors, andcolleagues only see the percentage completed, so they will judge yourperformance based on how bight the percentage is. Because the percentageis the only indicator a participant is judged by, participants areencouraged to get tasks done by any means and mark the task complete toincrease their percentage shown to superiors. This rush to mark taskscomplete at all costs can lead to poor work product that will causeproblems later in the project, or prevent the project from beingcompleted.

In large organizations, a CEO will be prevented from directly observingthe work product that goes into a project because he is on the otherside of the world or in charge of hundreds to tens of thousands ofproject participants. The size of teams and geographical layout of teamsrequires a CEO or project manager to look at summaries of the workcompleted. When CEO's use PC methods to predict a project outcome andreport the likelihood of the project completion to board members orshareholders, they often get the prediction wrong because they arelacking all of the necessary information.

In addition to the percentage of a project that has been completed, aCEO needs to know what areas of the project are experience problems, andif any of the work performed on the project is at a low enough standardto result in failures or delays further down the project timeline.Knowing which areas of the project are experiencing difficulties allowsthe CEO to re-allocate resources to ensure the project will be completedas planned.

In addition to needing information on problem areas, a CEO needs to knowwhich project participants are the best indicators regarding projectcompletion. When a CEO is looking at summaries he is unable to determinewhich project participant's reporting was considered more or less in thefinal report. Knowing that a seasoned and experienced employee thinks aproject is not going to be completed despite every other employee sayingthe project will be completed on time can weigh heavily on a CEOreporting to the shareholders if a project will be completed. Findingthe employee who is more accurate at indicating the likely outcome of aproject is made more difficult today when there can be hundreds or tensof thousands of employees reporting individually each day. Finding thesignal in the noise can mean a CEO gives an accurate report to boardmembers and shareholders, resulting in the CEO keeping his job.

The need remains for a method that allows for improved understanding ofwhether a project goal will be completed.

BRIEF SUMMARY OF THE INVENTION

Unless otherwise defined, herein all terms (including technical andscientific terms) have the same meaning as commonly understood by one ofordinary skill in the art to which this disclosure belongs. It may befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and may not be interpreted in an idealized or overlyformal sense unless expressly so defined herein.

The present disclosure provides for a method and system for predictingproject outcomes. The present method and system for predicting projectoutcomes may aid a project manager, a CEO or a company in quicklydetermining if projects are on schedule, and what groups, teams,departments, areas, or personnel need assistance in meeting projectgoals and deadlines.

In some embodiments, a dashboard may present a matrix summarizing theinput from project participant's encompassing their estimations ofactual completion of the project.

In some embodiments, a dashboard may illustrate a completion indicationrepresenting the probability that the project will be completed asplanned.

In some embodiments, a predictive index may provide for one or moreproject participants to indicate the project participant's ability topredict the probability of the completion of the goal or project.

The present disclosure addresses the shortcomings of prior systems andmethods.

Descriptions of certain illustrative aspects are described herein inconnection with the annexed FIGUREs. These aspects are indicative ofvarious non-limiting ways in which the disclosed subject matter may beutilized, all of which are intended to be within the scope of thedisclosed subject matter. Other advantages, emerging properties, andfeatures may become apparent from the following detailed disclosure whenconsidered in conjunction with the associated FIGUREs that are alsowithin the scope of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the disclosed subjectmatter will be set forth in any claims that are filed later. Thedisclosed subject matter itself, however, as well as a preferred mode ofuse, further objectives, and advantages thereof, will best be understoodby reference to the following detailed description of an illustrativeembodiment when read in conjunction with the accompanying drawings,wherein:

FIG. 1 presents a schematic diagram of a system for predicting outcomesin a computer network, according to some embodiments.

FIG. 2 illustrates a computer processing system within which the processmay operate, according to some embodiments.

FIG. 3 shows a process architecture according to some embodiments.

FIG. 4 depicts the process flow according to one embodiment of thedisclosed subject matter.

FIG. 5 depicts the process flow for a manager or CEO, according to someembodiments.

FIG. 6 depicts the workflow for how the embodiment performs goalcompletion prediction.

FIG. 7 separately shows an alternative workflow for how to perform agoal completion prediction.

FIG. 8 depicts an exemplary workflow for creating a user predictiveindex.

FIG. 9 depicts a second exemplary workflow for creating a userpredictive index, according to some embodiments.

FIG. 10 depicts an exemplary dashboard comprising a matrix output.

FIG. 11 presents an exemplary dashboard output comprising questionspresented to a participant.

FIG. 12 presents an exemplary method for calculating a completionindication.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Reference now should be made to the drawings, in which the samereference numbers are used throughout the different figures to designatethe same components.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an”, and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising” or“includes” and/or “including” when used in this specification, specifythe presence of stated features, regions, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, regions, integers, steps,operations, elements, components, and/or groups thereof. The terms“corpus” and “database” may be used interchangeably. As used herein, theterms “project participant” and “user” may be used interchangeably.Additionally, as used herein, the terms “project”, “objective”,“outcome”, and “goal” may be used interchangeably.

In addition to goal management functions, the organizational elements offlow up and flow down goals are captured in the definition of“projects”. “Project” is intended to include any organizational, social,business, or other activity, venture, task, program, plan, or task. Theterm “project” is not intended to be limiting to a business project. Auser may use the organizational elements for any purpose that involvesaligning various sub-departments or group efforts over time.

FIG. 1 presents a schematic diagram of an exemplary system embodimentfor predicting project outcomes as may be deployed across a computernetwork. The system for predicting project outcomes (10) may beimplemented in one or more computing devices (14), connected to thecomputer network (12). The computer network (12) may include multiplecomputing devices in communication with each other and with otherdevices or components through one or more wired and/or wireless datacommunication methods, where each communication method may comprise oneor more of wires, routers, switches, transmitters, or receivers. Thesystem for predicting project outcomes (10) and the computer network(12) may enable functionality for predicting project outcomes for one ormore users through their respective computing devices (20, 111, 18).Other embodiments of the inventive subject matter may be used withcomponents, systems, sub-systems, and/or devices other than thosedepicted herein.

The system for predicting project outcomes (10) may be configured toimplement a method for calculating a predictive index indication (22).For example, the system for predicting project outcomes (10) may receiveinput from the computer network (12), a corpus of electronic documents(16), a user, databases, other possible sources of input, or acombination thereof. In one embodiment, some or all of the inputs to thesystem (10) may be routed through the computer network (12). The variouscomputing devices (14) on the computer network (12) may include accesspoints for content creators and users. Some of the computing devices(14) may include devices for a database storing the corpus of data (16),which is shown as a separate entity in FIG. 1. Portions of the corpus ofdata (16) may also be provided on one or more other network attachedstorage devices, in one or more databases, or other computing devicesnot explicitly shown in FIG. 1. The computer network (12) may includelocal network connections and remote connections in various embodiments,such that the system for predicting project outcomes (10) may operate inenvironments of any size, including local and global, e.g., theinternet.

In one embodiment, the content creator creates content in a document ofthe corpus of data (16) for use as part of the corpus of data with thesystem for predicting project outcomes (10). The document may includeany file, text, article, psychological profile, past project data, pastinput, or any combination thereof, for use in the system for predictingproject outcomes (10). System users may access the system for predictingproject outcomes (10) through a network connection or an internetconnection to the computer network (12), and may provide input to thesystem for predicting project outcomes (10) through a network connectionor an internet connection to the computer network (12). In oneembodiment, the input may be formed using natural language. In anotherembodiment, the input may be formed by the user selecting an option suchas a color-coded button, or a scale.

The system for predicting project outcomes (10) may implement acalculation to generate a predictive index indication (22), whichcomprises a plurality of stages for processing user input and the corpusof data (16), and generates a predictive index indication. The systemfor predicting project outcomes (10) may also implement a calculation togenerate a project completion indication (24), which comprises aplurality of stages for processing user input and the corpus of data(16), and generates an indication of the likelihood of a projectcompletion. The predictive index indication (22) and the projectcompletion indication (24) will be described in greater detail withregard to FIG. 3.

FIG. 2 illustrates a computer processing system within which the processof the present disclosure may operate. The data capture, analysis, anduse of the method and system of the present disclosure may employ theuse of a computing system associated with a three-dimensional camerasystem. Thus, with reference to FIG. 2, an exemplary system withincomputing environment (50) for implementing the disclosure includes ageneral purpose computing device in the form of computing system (52),commercially available from, for example, Intel, IBM, AMD, Apple,Motorola, Cyrix, etc. Components of computing system (54) may include,but are not limited to, processing unit (56), system memory (58), andsystem bus (60) that may couple various system components, includingsystem memory (58) to processing unit (56). System bus (60) may be anyof several types of bus structures including a memory bus or memorycontroller, a peripheral bus, or a local bus using any of a variety ofbus architectures.

Computing system (52) includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computing system (52) and includes both volatile and nonvolatilemedia, and removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data.

Computer memory includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by computing system (52).

System memory (58) includes computer storage media in the form ofvolatile, nonvolatile memory, or the combination thereof, such as readonly memory (ROM) (62) and random access memory (RAM) (64). A basicinput/output system (BIOS) (66), containing the routines that help totransfer information between elements within computing system (52), suchas during start-up, may be stored in ROM (62). RAM (64) may containdata, program modules, or the combination thereof, that are immediatelyaccessible to and/or presently being operated on the processing unit(56). Some embodiments may further comprise an operating system (68),application programs (70), other program modules (72), and program data(74).

Computing system (52) may also comprise other removable/non-removable,volatile/nonvolatile computer storage media. In some embodiments, a harddisk drive (76) may read or write to a non-removable, nonvolatilemagnetic media, a magnetic disk drive (78) that may read or write toremovable, nonvolatile magnetic disk (80), and an optical disk drive 82that reads from or writes to removable, nonvolatile optical disk 84 suchas a CD ROM or other optical media could be employed to store theinvention of the present embodiment. Other removable/non-removable,volatile/nonvolatile computer storage media that can be used in theexemplary operating environment include, but are not limited to,magnetic tape cassettes, flash memory cards, digital versatile disks,digital video tape, solid state RAM, solid state ROM, and the like.

The hard disk drive 76 may be connected to the system bus (60) through anon-removable memory interface (86) A magnetic disk drive (78) andoptical disk drive (82) may be connected to the system bus (60) by aremovable memory interface (88).

The drives and their associated computer storage media, discussed above,may provide storage of computer readable instructions, data structures,program modules and other data for computing system (52). In someembodiments, hard disk drive (76) is illustrated as storing operatingsystem 90, application programs 92, other program modules 94 and programdata 96. Note that these components can either be the same as ordifferent from operating system (68), application programs (70), otherprogram modules (72), and program data (74). Operating system 90,application programs 92, other program modules 94, and program data 96are given different numbers here to illustrate that, at a minimum, theyare different copies.

A participant may enter commands and information into the computingsystem (52) through input devices, such as tablet or electronicdigitizer (98), microphone (100), keyboard (102), pointing device (104),or combination thereof. The pointing device may be any one of a mouse,trackball, or touch pad. The input devices may be connected to theprocessing unit (56) through a participant input interface (106) coupledto the system bus (60). In some embodiments, the processing unit (56)may be connected by other interface and bus structures, such as aparallel port, game port or a universal serial bus (USB).

Monitor (108) may be connected to the system bus (60) via a videointerface (110). In some embodiments, display 108 may also be integratedwith a touch-screen panel (112) or the like.

In some embodiments, the monitor, the touch screen panel, or combinationthereof, may be physically coupled to a housing in which computingsystem 52 is incorporated, such as, for example, in a tablet-typepersonal computer or smart phone.

In some embodiments, the computing system (52) may also include otherperipheral output devices such as speakers 114, printer 116, or thecombination thereof, connected through an output peripheral interface118 or the like.

In some embodiments, computing system (52) may operate in a networkedenvironment using logical connections to one or more remote computingsystems (120). The remote computing system (120) may be a personalcomputer, mobile electronic devices, a server, a router, a network PC, apeer device or other common network node. The remote computing system(120) may comprise one or more of the elements described above relativeto computing system (52), although only a memory storage device (122)has been illustrated.

The logical connections depicted in FIG. 2 may include a local areanetwork (LAN) (124) connected through network interface (126), a widearea network (WAN) 128, or combination thereof, connected via modem(130). In some embodiments, the logical connection may also includeother networks such as mobile telephone service networks. Suchnetworking environments are utilized in offices, enterprise-widecomputer networks, intranets, mobile networks, and the Internet.

For example, in the present embodiment, computer system (52) maycomprise the source machine from which data may be generated/transmittedand the remote computing system 120 may comprise the destinationmachine. Note however that source and destination machines need not beconnected by a network or any other means, but instead, data may betransferred via any media capable of being written by the sourceplatform and read by the destination platform or platforms.

In another example, in the present embodiment, remote computing system120 may comprise the source machine from which data is beinggenerated/transmitted and computer system 52 may comprise thedestination machine.

In a further embodiment, in the present disclosure, computing system 52may comprise both a source machine from which data is beinggenerated/transmitted and a destination machine. The remote computingsystem 120 may also comprise both a source machine from which data isbeing generated/transmitted and a destination machine.

Referring to FIG. 2, for the purposes of this disclosure, it will beappreciated that remote computer 120 may include any suitable term suchas, but not limited to “device”, “processor based mobile device”,“mobile device”, “electronic device”, “processor based mobile electronicdevice”, “mobile electronic device”, “wireless electronic device”, or“location-capable wireless device,” including a smart phone or tabletcomputer.

The central processor operating pursuant to operating system softwaresuch as, but not limited to, Apple IOS®, Google Android® IBM OS/2®,Linux®, UNIX®, Microsoft Windows®, Apple Mac OSX®, and othercommercially available operating systems provides functionality for theservices provided by the present invention. The operating system orsystems may reside at a central location or distributed locations (i.e.,mirrored or standalone).

Software programs or modules instruct the operating systems to performtasks such as, but not limited to, facilitating client requests, systemmaintenance, security, data storage, data backup, data mining,document/report generation, and algorithm generation. The providedfunctionality may be embedded directly in hardware, in a software moduleexecuted by a processor, or in any combination of the two.

Furthermore, software operations may be executed, in part or wholly, byone or more servers or a client's system, via hardware, software moduleor any combination of the two. A software module (program or executable)may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROMmemory, registers, hard disk, a removable disk, a CD-ROM, DVD, opticaldisk, or any other form of storage medium known in the art. An exemplarystorage medium is coupled to the processor such that the processor canread information from, and write information to, the storage medium. Inthe alternative, the storage medium may be integral to the processor.The processor and the storage medium may also reside in an applicationspecific integrated circuit (ASIC). The bus may be an optical orconventional bus operating pursuant to various protocols that are wellknown in the art.

FIG. 3 shows a process architecture for an embodiment of the predictionsystem (300). When predicting the completion of a planned currentproject or goal, important variables to consider include past input froma project participant regarding their prediction of completed goal, thequality of work performed towards that goal, and whether the goal wascompleted successfully.

A project outcome calculation module (320) and a predictive indexcalculation module (340) may obtain information stored in the storagemodule (325) that includes past input from a project participantregarding their prediction of whether a goal will be completed, thequality of work performed towards that goal, and if the goal wascompleted successfully. The output from the project outcome calculationmodule (320) and the predictive index calculation module (340) may bestored in the storage module (325). The input from a project participantis received through the input module (310). The storage module (325) mayalso comprise historical data, which may include historical datarelating to one or more project participant's. The historical data maycomprise one or more of the project participant's past projects, anoutcome of the past projects, a project participant's past predictedoutcome of the past projects, a score indicating project participant'sability to predict project outcomes, or any combination thereof.

Some project participant may show a history of accurate predictions of agoal outcome. In this case, calculations may be altered to weigh moreheavily the input from specific project participants.

A method of calculating and predicting a goal completion may include:(1) determining the total number of sub-goals that go into the goalwhose outcome is being predicted, (2) grouping the sub-goals into aspecific group which may include marketing goals, engineering goals, orsales goals, (3) dividing the grouped sub-goals by the total number ofsub-goals, and (4) multiplying that number by the average predictioninput from project participant's in the specific group. For improvedaccuracy, the number may be multiplied by a specific weight given by anexpert or determined through predictive algorithms, or data analysisalgorithms. Additionally, the level in the goal hierarchy is anotherpossible factor in determining the weight of each metric.

Another exemplary method for calculating and predicting goal completionand/or determining predictive variables may include using historicaldata, specifically the associations between variables and the actualoutcomes of goals, and a predictive model methodology, such as logisticregression, multinomial logistic regression, linear regression, supportvector machine learning, a Bayesian classifier, a decision treeclassifier, a copula-based classifier, a k-nearest neighbors classifier,a random forest classifier, neural networks, and boosting algorithms.

In some embodiments, a project outcome calculation module (320) may beemployed to update the goal prediction models. The goal predictionmodels may be updated in response to an event such as a projectparticipant's input, a goal being marked complete, the availability ofnew historical data, or other changes to data that is made available tothe goal prediction model.

The predictive index method (327) indicates the project participant'sability to correctly predict the outcome of the project. The predictiveindex for the user may be generated manually by a project managementexpert, automatically using machine learning techniques, or acombination thereof. Adjustments to the predictive index may be made toweigh certain data sets as more important than other data sets. Weighingof data sets and data points may be made manually by a user withpermission to make changes. For example, a project management expert maydecide that adjustments to how much certain information is weighted inthe predictive index. The expert may then make those adjustments withinthe software. The adjustments may be to all previous data, in which casea new calculation would be performed to update one or more projectparticipants predictive index. However, the adjustments made by theexpert may only impact calculations in the future, which would notrequire recalculating past predictive index results.

Machine learning techniques that may be used to automatically calculateand generate a predictive index for a project participant may include,but are not limited to: a Collective Matrix Factorization (CMF)technique, a Principal Component Analysis (PCA) technique, aNon-negative Matrix Factorization (NMF) technique, a CanonicalCorrelation Analysis technique (CCA), or an Inter-Battery FactorAnalysis (IBFA) technique. There may be hundreds or even thousands oflearning techniques applied, each of which performs different analysisto generate a predictive index.

The predictive index calculation module (340) used for calculating thepredictive index may further be configured to generate and/or update themachine learning techniques from historical data associated projectparticipant's as the historical data is updated.

The variables used to calculate the predictive index may include, butare not limited to, one or more parameters associated with the projectparticipant, or multiple project participant's, such as, past input fromthe project participant including quality of work statement, predictedlikelihood of the goal being completed, and actual outcome of the goalfor which the project participant was predicting the outcome anddetermining the work quality. The parameters may also include time,date, weather, other project participant's, payment or salary,psychological screening, psychological analysis tests, psychometricassessments, career tests, IQ tests, emotional intelligence tests,personality tests, sentiment analysis, progress into current goalperiod, organizational relationship to other project participant's,organizational levels, and project participant's role in theorganization. In some embodiments, the variables may include any datathat may impact the outcome of the goal.

A hierarchical organization is an organizational structure where everyentity in the organization, except one, is subordinate to a single otherentity. Organizational data may relate to the organization in which theproject participants and project managers are members. Theorganizational data may comprise information on the hierarchy ofprojects, or information on the hierarchy of project participants andproject managers.

The results of the calculation by the project outcome calculation module(320) or predictive index calculation module (340) may be presented to auser through the presentation module (345).

FIG. 4 depicts the process flow for a project participant according tosome embodiments of the present disclosure. In the project participantflow (440), step (442) represents a project participant accessing aportal or user interface that gives the project participant access tothe system. The project participant may be presented with multiplequestions as depicted in step (444). Possible questions include: what isthe quality of work that is being performed towards the current project,or what is the likelihood that the current project will be completed asplanned. In step (446) the project participant may provide answers tothe questions presented in step (444). The project participant may beable to view a dashboard in step (448), where information regarding thecurrent project may be presented. Information contained in the dashboardmay include a matrix depicting a summary of other project participant'sanswers, input, responses, or a combination thereof, to the questionspresented in step (444). The dashboard may also present an indication ofthe likelihood of the current project being completed as planned. Fromthe dashboard, the project participant may have access to viewing theirpersonal predictive index/score in step (450). Variations of this flowmay include the project participant not being able to view other projectparticipant's input as a matrix.

FIG. 5 depicts the process flow for a project manager (500) businessmanager or CEO according to some embodiments of the present disclosure.The flow begins with the project manager, business manager, or CEOaccessing the portal to the system in step (542). The CEO may bepresented with multiple questions as depicted in step (544). Possiblequestions include: 1) “What is the quality of work being performedtowards the current project or the percentage of work completed?”,and/or 2) “What is the likelihood that the current project will becompleted as planned?”, or combinations thereof. In step (546) the CEOmay provide answers to the questions presented in step (544). The CEOmay then be able to view a dashboard in step (548), where informationregarding the current project may be presented. Information contained inthe dashboard may include a matrix depicting a summary of other CEOanswers, input, responses, or combinations thereof, to the questionspresented in step (544). The dashboard may also present an indication ofthe likelihood of the current project being completed as planned. Fromthe dashboard, the CEO may have access to viewing and accessinghistorical and current information of project participant's, groupswithin the organization, a task, a goal, or other components of theproject in step (552). To access the information (552) the CEO mayselect the project participant, task, subgroup of the organization, orother components of the project (550). The CEO may additionally messageor otherwise communicate with a selected project participant or multipleproject participant's that have been selected (554).

In some embodiments, the CEO may not be presented with questions (544),and thus, the CEO will not proceed with providing answers in step (546).Instead the CEO will be presented with the dashboard (548) immediatelyafter accessing the portal (542).

FIG. 6 shows a process flow (610) for the method and system of thepresent disclosure, including, at a functional level, components thatmay be associated for calculating and generating a project completionindication. In some embodiments, the operation may start with obtaininghistorical data (612) related to one or more project participant's.Second, the process may obtain, from one or more project participant's,a quality indication input (614). The quality indication is related tothe quality of work performed on the current project, as determined bythe project participant. Third, the process may obtain, from one or moreproject participant's, a prediction indication input (616). Theprediction indication is related to the project participant's predictedoutcome of the current project, as determined by the projectparticipant. Fourth, the process may calculate a completion indexrelated to the current project (618). The completion index is related tothe likelihood of the current project being marked complete. Finally,the process may present the completion indication and a matrix andmetrics of all project participant's input to one or more users (620).

FIG. 7 separately shows an alternative workflow (710) for how thepresent disclosure performs goal completion prediction. The process maybegin with obtaining historical data (712) related to one or moreproject participant's. Second, the process may obtain, from one or moreproject participant's, a quality indication input (714). The qualityindication is related to the quality of work performed by the projectparticipant toward the current project, as determined by the projectparticipant. Third, the process may obtain, from one or more projectparticipant's, a prediction indication input (716). The predictionindication is related to the project participant's predicted outcome ofthe current project, as determined by the project participant. Fourth,the process may calculate a completion index related to the currentproject (718). The completion index is related to the likelihood of thecurrent project being marked complete. Fifth, the process may use thecalculated completion index of the current project and other associatedprojects to calculate the completion index of higher-level projects.Lastly, the process may present the completion indication and a matrixand metrics of all project participant's input to one or more user's(720).

FIG. 8 shows an exemplary workflow for how the present disclosurecreates a user predictive index (810). In some embodiments the operationmay begin by obtaining historical data (812) related to one or moreproject participant's. Second, the process may calculate, utilizing thehistorical data, a prediction index related to the project participant(814). Finally, the process may store the project participant'sprediction index in the historical data associated with the projectparticipant (816).

FIG. 9 separately shows an alternative workflow for how the presentdisclosure performs goal completion prediction (910). In someembodiments, the operation may begin by obtaining historical data (912)related to one or more project participant's. Second, the process maycalculate, utilizing the historical data, a prediction index related tothe project participant (914). Third, the process may store the projectparticipant's prediction index in the historical data associated withthe project participant (916). Lastly, the process may use thecalculated predictive index of project participant to calculate thecompletion index of the current project and higher-level projects (918).

At FIG. 10 illustrates a Question Dashboard (1010) where a firstquestion (1012) and a second question (1016) are generated and presentedto a user. The questions that may be presented to the projectparticipant or project manager include: 1) “How likely are you toachieve this goal?” (1012), and/or 2) “How do you feel about the qualityof work done so far?” (1016). A user may select from input options(1014, 1018) that may be presented as shapes, colors, or text.

At FIG. 11 illustrates an exemplary Matrix Dashboard (1110) output asmay be generated and presented to a user by embodiment through agraphical user interface that is. The matrix dashboard (110) groupssimilar responses (1112) from multiple project participant's andpresents them on a matrix grid format. This view allows a projectmanager to easily see the responses from project participant's and seeif any project participant's are at risk of not completing their tasks.Responses that indicate a greater risk of not completing the assignedtask are shown towards the lower left corner (1114), whereas theresponses that indicate high success of completion are grouped towardsthe upper right of the matrix (1116).

FIG. 12 presents an exemplary method for calculating a completionindication (1210). One method of calculating and predicting a goalcompletion indication (1210) may include: determining the total numberof sub-goals (1214,1216,1218) that go into the goal whose outcome isbeing predicted (1212); grouping the sub-goals (1216,1218) into aspecific group (1214) which may include marketing goals, engineeringgoals, or sales goals; dividing the grouped sub-goals (1214,1216,1218)by the total number of sub-goals going into the goal whose outcome isbeing predicted (1212), then multiplying that number by the averageprediction input from project participant's in the specific group; andfinally, adding up the results from each specific group (1214) to getthe user predictive index (1210). For improved accuracy, the number maythen be multiplied by a specific weight given by an expect or determinedthrough predictive algorithms or data analysis algorithms.

In one embodiment, a computer-implemented method for predicting currentproject outcomes is described, providing a project manager with thecertainty of those outcomes occurring, what projects participant's aremost at risk of not completing their work, and what project tasks are atthe most risk of not being completed for the project, the methodcomprising: obtaining, by a computing system, organizational datarelating to the organization in which the project participants andproject manager are members, wherein the organizational data comprises:information on the hierarchy of projects; information on the hierarchyof project participants and project managers; obtaining, by thecomputing system, historical data relating to one or more projectparticipant's, wherein the historical data comprises: one or more of theproject participant's past projects; an outcome of the past projects; aproject participant's past predicted outcome of the past projects; and ascore indicating project participant's ability to predict projectoutcomes; obtaining, by the computing system, from one or more projectparticipant's, a quality indication, wherein the quality indication maybe related to the quality of work performed by the project participanttoward the current project; and a prediction indication, wherein theprediction indication may be related to the project participant'spredicted outcome of the current project; calculating, by the computingsystem, a completion indication, wherein the completion indication maybe related to the likelihood of the current project being markedcomplete, wherein, the completion indication may be calculated using oneor more predictive models and data comprising: organizational data, oneor more project participant's historical data, one or more projectparticipant's quality indication, and one or more project participant'sprediction indication; presenting, by the computing system, a completionindication and a matrix indicating one or more project participant'squality indication and predication indication. The completion indicationmay be calculated from data further comprising: a plurality ofsub-projects and a plurality of subordinate project participant's,wherein the plurality of sub-projects and subordinate projectparticipant's are determined by the organizational data. The scoreindicating project participant's ability to predict project outcomes maybe calculated by the method comprising: obtaining, by the computingsystem, organizational data relating to the organization in which theproject participants and project manager are members, wherein theorganizational data comprises: information on the hierarchy of projects;information on the hierarchy of project participants and projectmanagers; obtaining, by the computing system, historical data relatingto the project participant's, wherein the historical data comprises: oneor more past projects relating to the project participant; one or moreoutcomes relating to the one or more past projects; and one or more pastprediction indications relating to the one or more past projectscalculating, by the computing system, from the historical data, apredictive index indicating the project participant's ability to predictproject outcomes.

In one embodiment, a computerized system for for predicting currentproject outcomes is described, providing a project manager with thecertainty of those outcomes occurring, and what projects participant'sare most at risk of not completing their work for the project, thesystem comprising: one or more processors; and a memory comprisinginstructions that, when executed by the one or more processors, causethe one or more processors to perform: obtaining organizational datarelating to the organization in which the project participants andproject manager are members, wherein the organizational data comprises:information on the hierarchy of projects; information on the hierarchyof project participants and project managers; obtain historical datarelating to one or more project participant's, wherein the historicaldata comprises: one or more of the project participant's past projects;an outcome of the past projects; a project participant's past predictedoutcome of the past projects; and a score indicating projectparticipant's ability to predict project outcomes; obtain, from one ormore project participant's, a quality indication, wherein the qualityindication may be related to the quality of work performed by theproject participant toward the current project; and a predictionindication, wherein the prediction indication may be related to theproject participant's predicted outcome of the current project;calculate a completion indication, wherein the completion indication maybe related to the likelihood of the current project being markedcomplete, wherein, the completion indication may be calculated using oneor more predictive models and data comprising: organizational data, oneor more project participant's historical data, one or more projectparticipant's quality indication, and one or more project participant'sprediction indication; present a completion indication and a matrixindicating one or more project participant's quality indication andpredication indication. The completion indication may be calculated fromdata further comprising: a plurality of sub-projects and a plurality ofsubordinate project participant's, wherein the plurality of sub-projectsand subordinate project participants are determined by theorganizational data. The score indicating project participant's abilityto predict project outcomes may be calculated by instructions stored onthe memory that, when executed by the one or more processors, cause theone or more processors to: obtaining organizational data relating to theorganization in which the project participants and project manager aremembers, wherein the organizational data comprises: information on thehierarchy of projects; information on the hierarchy of projectparticipants and project managers; obtain historical data relating tothe project participant's, wherein the historical data comprises: one ormore past projects relating to the project participant; one or moreoutcomes relating to the one or more past projects; and one or more pastprediction indications relating to the one or more past projectscalculate, from the historical data, a predictive index indicating theproject participant's ability to predict project outcomes.

In one embodiment, a computer readable medium comprising a system forfor predicting current project outcomes is described, providing aproject manager with the certainty of those outcomes occurring, and whatprojects participants are most at risk of not completing their work forthe project, the computer readable medium comprising instructions for:obtaining organizational data relating to the organization in which theproject participants and project manager are members, wherein theorganizational data comprises: information on the hierarchy of projects;information on the hierarchy of project participants and projectmanagers; obtaining historical data relating to one or more projectparticipant's, wherein the historical data comprises: one or more of theproject participant's past projects; an outcome of the past projects; aproject participant's past predicted outcome of the past projects; and ascore indicating project participant's ability to predict projectoutcomes; obtaining, from one or more project participant's, a qualityindication, wherein the quality indication may be related to the qualityof work performed by the project participant on the current project; anda prediction indication, wherein the prediction indication may berelated to the project participant's predicted outcome of the currentproject; calculating a completion indication, wherein the completionindication may be related to the likelihood of the current project beingmarked complete, wherein, the completion indication may be calculatedusing one or more predictive models and data comprising: organizationaldata, one or more project participant's historical data, one or moreproject participant's quality indication, and one or more projectparticipant's prediction indication; presenting a completion indicationand a matrix indicating one or more project participant's qualityindication and predication indication. The completion indication may becalculated from data further comprising: a plurality of sub-projects anda plurality of subordinate project participant's, wherein the pluralityof sub-projects and subordinate project participant's are determined bythe organizational data. The score indicating project participant'sability to predict project outcomes may be calculated by the methodcomprising: obtaining organizational data relating to the organizationin which the project participants and project manager are members,wherein the organizational data comprises: information on the hierarchyof projects; information on the hierarchy of project participants andproject managers; obtaining historical data relating to the projectparticipant's, wherein the historical data comprises: one or more pastprojects relating to the project participant; one or more outcomesrelating to the one or more past projects; and one or more pastprediction indications relating to the one or more past projects;calculating, from the historical data, a predictive index indicating theproject participant's ability to predict project outcomes.

The embodiments described above are exemplary and are not to be taken aslimiting in any way. They are merely illustrative of the principles ofthe disclosure. Various changes, modifications and alternatives will beapparent to one skilled in the art. Accordingly, it is intended that theart disclosed shall be limited only to the extent required by theappended claims and the rules and principles of applicable law.

1. A computer-implemented method for predicting an outcome of a currentat least one business project, business objective, and business goal, byutilizing instructions in a non-transitory computer-readable mediumstored in a computing system, the method comprising: obtaining, by thecomputing system, organizational data relating to an organization inwhich at least one participant and at least one manager are members,wherein the organizational data comprises: information on a hierarchy ofthe at least one business project, business objective, and businessgoal; information on a hierarchy of the at least one participant and theat least one manager; obtaining, by the computing system, historicaldata relating to the at least one participant, wherein the historicaldata comprises: at least one past business project, past businessobjective, and past business goal performed by the at least oneparticipant; an outcome of the at least one past business project, pastbusiness objective, and past business goal; a past predicted outcome ofthe at least one participant's at least one past business project, pastbusiness objective, and past business goal; and a score indicating theat least one participant's ability to predict the outcome of the atleast one past business project, past business objective, and pastbusiness goal; obtaining, by the computing system, from the at least oneparticipant, a quality indication, wherein the quality indication isrelated to the quality of work performed by the at least one participanttoward the current at least one business project, business objective,and business goal; and a prediction indication, wherein the predictionindication is related to the at least one project participant'spredicted outcome of the current at least one business project, businessobjective, and business goal; calculating, by the computing system, acompletion indication, wherein the completion indication is calculatedfrom data comprising a likelihood of the current at least one businessproject, business objective, and business goal being marked complete,participants among the at least one participant whom are most at risk ofnot completing their work, and business projects, business objectives,and business goals which are most at risk of not being completed;wherein, the completion indication is further calculated using one ormore predictive models and data comprising: the organizational data, theat least one participant's historical data, the at least oneparticipant's quality indication, and the at least one participant'sprediction indication; and presenting, by the computing system, acompletion indication and a matrix indicating the at least oneparticipant's quality indication and the at least one participant'spredication indication.
 2. The computer-implemented method of claim 1,wherein the completion indication is further calculated from datafurther comprising: a plurality of sub-business projects, sub-businessobjectives, and sub-business goals, and a plurality of subordinateparticipants, wherein the plurality of sub-business projects,sub-business objectives, and sub-business goals and subordinate projectparticipants are determined by the organizational data.
 3. Thecomputer-implemented method of claim 1, wherein the score indicating theat least one participant's ability to predict outcomes of the at leastone business project, business objective, and business goal iscalculated by utilizing additional instructions in the non-transitorycomputer-readable medium to perform a method comprising: obtaining, bythe computing system, the organizational data relating to theorganization in which the at least one participant and the at least onemanager are members, wherein the organizational data comprises: theinformation on the hierarchy of the at least one current businessproject, business objective, and business goal; the information on thehierarchy of the at least one participant and the at least one manager;obtaining, by the computing system, the historical data relating to theat least one participant; calculating, by the computing system, from thehistorical data, a predictive index indicating the at least oneparticipant's ability to predict the outcome of the outcome of the atleast one business project, business objective and business goal.
 4. Acomputerized system for predicting an outcome of a current at least onebusiness project, business objective, and business goal the systemcomprising: at least one processor; and at least one non-transitorycomputer-readable medium stored in the at least one processor, the atleast one non-transitory computer-readable medium comprisinginstructions that, when executed by the at least one processor, causesthe at least one processor to: obtain organizational data relating to anorganization in which at least one participant and at least one managerare members, wherein the organizational data comprises: information on ahierarchy of the current at least one business project, businessobjective and business goal; information on a hierarchy of the at leastone participant and the at least one manager; obtain historical datarelating to the at least one participant, wherein the historical datacomprises: the at least one participant's past business projects,business objectives, and business goals; an outcome of the at least onepast business projects, business objectives, and business goals; the atleast one participant's past predicted outcome of the at least one pastbusiness project outcome, business objective outcome, and business goaloutcome; and a score indicating the at least one participant's abilityto predict the at least one past business project outcome, businessobjective outcome, and business goal outcome; obtain, from the at leastone participant, a quality indication, wherein the quality indication isrelated to the quality of work performed by the at least one participanttoward the current at least one business project, business objective,and business goal; and a prediction indication, wherein the predictionindication is related to the at least one participant's predictedoutcome of the current at least one business project, businessobjective, and business goal; calculate a completion indication, whereinthe completion indication is related to a likelihood of the current atleast one business project, business objective, and business goal beingmarked complete, participants among the at least one participant whomare most at risk of not completing their work, and business projects,business objectives, and business goals which are most at risk of notbeing completed; wherein, the completion indication is calculated usingone or more predictive models and data comprising. the organizationaldata, the at least one participant's historical data, the at least oneparticipant's quality indication, and the at least one participant'sprediction indication; present a completion indication and a matrixindicating the at least one participant's quality indication andpredication indication.
 5. The computerized system of claim 4, whereinthe completion indication is calculated from data further comprising: aplurality of sub-business project, sub-business objectives andsub-business goals and a plurality of subordinate participants, whereinthe plurality of sub-business projects, sub-business objectives andsub-business goals and subordinate participants are determined by theorganizational data.
 6. The computerized system of claim 4, wherein thescore indicating participant's ability to predict the outcomes ofbusiness projects, business objectives, and business goals is calculatedby additional instructions in the non-transitory computer-readablemedium that, when executed by the at least one processor, causes the atleast one processor to: obtain organizational data relating to theorganization in which the at least one participant and at least onemanager are members, wherein the organizational data comprises:information on a hierarchy of the current at least one business project,business objective, and business goal; information on a hierarchy of theat least one participant and at least one manager; obtain historicaldata relating to the at least one participant, wherein the historicaldata comprises: one or more past business projects, past businessobjectives, and past business goals relating to the at least oneparticipant; at least one outcome relating to at least one past businessproject, past business objective, and past business goal; and at leastone past prediction indication relating to the at least one pastbusiness project, past business objective, and past business goal; andcalculate, from the historical data, a predictive index indicating theat least one participant's ability to predict the outcome of the atleast one business project, business objective and business goal.
 7. Anon-transitory computer-readable medium adapted to be stored in acomputing system for predicting an outcome of a current at least onebusiness project, business objective, and business goal, thecomputer-readable medium comprising instructions for: obtainingorganizational data relating to an organization in which at least oneparticipant and at least one manager are members, wherein theorganizational data comprises: information on a hierarchy of businessprojects, business objectives, and business goals; information on ahierarchy of the at least one participant and the at least one manager;obtaining historical data relating to the at least one participant,wherein the historical data comprises: at least one past businessproject, business objective, and business goal performed by the at leastone participant; at least one outcome of the at least one past businessproject, business objective, and business goal; a past predicted outcomeof the at least one past business project, business objective, andbusiness goal; and a score indicating the at least one participant'sability to predict the outcomes of the at least one past businessproject, business objective, and business goal; obtaining, from the atleast one participant, a quality indication, wherein the qualityindication is related to a quality of work performed by the at least oneparticipant on the current at least one business project, businessobjective, and business goal; and a prediction indication, wherein theprediction indication is related to the at least one participant'spredicted outcome of the current at least one business project, businessobjective, and business goal; calculating a completion indication,wherein the completion indication is calculated from data comprising alikelihood of the current at least one business project, businessobjective, and business goal being marked complete, participants amongthe at least one participant whom are most at risk of not completingtheir work, and the business projects, business objectives, and businessgoals which are most at risk of not being completed; wherein, thecompletion indication is calculated using one or more predictive modelsand data comprising: the organizational data, the at least oneparticipant's historical data, the at least one participant's qualityindication, and the at least one participant's prediction indication;and presenting a completion indication and a matrix indicating the atleast one participant's quality indication and predication indication.8. The non-transitory computer: readable medium of claim 7, wherein thecompletion indication is calculated from data further comprising: aplurality of sub-business projects, sub-business objectives, andsub-business goals, and a plurality of subordinate participants, whereinthe plurality of sub-business projects, sub-business objectives, andsub-business goals and subordinate participants are determined by theorganizational data.
 9. The non-transitory computer-readable medium ofclaim 7, wherein the score indicating the at least one participant'sability to predict outcomes is calculated by a method comprising:obtaining the organizational data relating to the organization in whichthe at least one project participant and the at least one manager aremembers and; calculating, from the historical data, a predictive indexindicating the participant's ability to predict the outcomes.